摘 要
随着工业4.0的推进和智能制造技术的发展，运动控制技术在自动化机械系统中的应用日益广泛，其性能直接影响系统的效率与精度因此，本研究聚焦于自动化机械系统中运动控制技术的关键问题，旨在探索高效、精准且适应性强的运动控制策略以满足复杂工业场景的需求研究通过分析传统运动控制方法的局限性，结合现代控制理论与人工智能技术，提出了一种基于自适应预测算法的新型运动控制方案该方案融合了模型预测控制与深度学习的优势，能够实时调整控制参数并优化轨迹规划同时，针对实际工业环境中的干扰因素，引入了鲁棒控制机制以提升系统的抗干扰能力实验结果表明，所提出的控制方法在动态响应、轨迹跟踪精度及能耗优化等方面均表现出显著优势与传统方法相比，新方案可将定位误差降低约30%，并有效缩短了调节时间此外，本研究还开发了一套集成化运动控制平台，实现了对复杂多轴系统的协同控制这一创新成果为自动化机械系统的智能化升级提供了重要技术支持最终结论显示，基于自适应预测算法的运动控制技术不仅具备较高的实用价值，还为未来相关领域的研究奠定了理论与实践基础关键词：运动控制技术；自适应预测算法；模型预测控制；深度学习；鲁棒控制机制
Abstract
With the advancement of Industry 4.0 and the development of smart manufacturing technologies, motion control technology has become increasingly prevalent in automated mechanical systems, with its performance directly impacting system efficiency and accuracy. Consequently, this study focuses on the critical issues of motion control technology in automated mechanical systems, aiming to explore highly efficient, precise, and adaptable motion control strategies to meet the demands of complex industrial scenarios. By analyzing the limitations of traditional motion control methods and integrating modern control theory with artificial intelligence techniques, a novel motion control scheme based on adaptive predictive algorithms is proposed. This approach combines the advantages of model predictive control and deep learning, enabling real-time adjustment of control parameters and optimization of trajectory planning. Additionally, to address interference factors in actual industrial environments, a robust control mechanism is introduced to enhance the system's disturbance rejection capability. Experimental results demonstrate that the proposed control method exhibits significant advantages in dynamic response, trajectory tracking accuracy, and energy consumption optimization. Compared with traditional methods, the new scheme reduces positioning errors by approximately 30% and effectively shortens settling time. Furthermore, this study develops an integrated motion control platform that achieves coordinated control of complex multi-axis systems. This innovative outcome provides crucial technical support for the intelligent upgrading of automated mechanical systems. The final conclusion indicates that motion control technology based on adaptive predictive algorithms not only possesses high practical value but also lays a theoretical and practical foundation for future research in related fields..Key Words：Motion Control Technology；Adaptive Predictive Algorithm；Model Predictive Control；Deep Learning；Robust Control Mechanism
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 自动化机械系统的发展背景 1
1.2 运动控制技术的研究意义 1
1.3 国内外研究现状分析 1
1.4 本文研究方法与技术路线 2
第2章 运动控制技术的基础理论 3
2.1 运动控制的基本概念与原理 3
2.2 控制算法在运动系统中的应用 3
2.3 传感器技术对运动控制的影响 4
2.4 动力学建模与仿真分析 5
2.5 关键技术难点与挑战 5
第3章 自动化机械系统的控制策略研究 7
3.1 精确控制的实现方法 7
3.2 基于反馈的闭环控制系统设计 7
3.3 智能控制技术的应用分析 8
3.4 多轴协同运动控制策略 8
3.5 实时性与稳定性优化研究 9
第4章 运动控制技术的实际应用与案例分析 10
4.1 工业机器人中的运动控制实践 10
4.2 数控机床的高精度控制技术 10
4.3 自动化流水线的动态协调控制 11
4.4 特殊环境下的运动控制需求 11
4.5 应用效果评估与改进建议 12
结 论 13
参考文献 14
致 谢 15
